latent-class-model

Estimation of Gaussian Process - Latent Class Choice Models (GP-LCCM) using the Expectation Maximization (EM) algorithm

Estimation of Gaussian Bernoulli Mixture - Latent Class Choice Models (GBM-LCCM) using the Expectation Maximization (EM) algorithm

Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) <https://doi.org/10.1007/s11336-019-09693-2>

Code for Ren, B. and Barnett, I. (2022), Autoregressive mixture models for clustering time series. J. Time Ser. Anal., 43: 918-937. https://doi.org/10.1111/jtsa.12644 https://onlinelibrary.wiley.com/doi/abs/10.1111/jtsa.12644

Code for the paper `Head-to-head comparison between FibroTest and ELF, for the fibrosis diagnostic in outpatients with type-2 diabetes, using Bayesian latent class models`

Replication materialss for: Heterogeneous Preferences for Use and Non-use Values Provided by Insects (People and Nature, 2026)

Discrete-choice (conjoint) model predicting which of 4 car safety-feature bundles a buyer picks - 3rd public / 4th private on Kaggle, logloss 1.185. R only. SUTD Analytics Edge 2026.